{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# pandas中绘制图表"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_table('data-2017.txt', sep=' ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>城市</th>\n",
       "      <th>平均工资</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>北京市</td>\n",
       "      <td>9227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>天津市</td>\n",
       "      <td>5729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>河北省</td>\n",
       "      <td>4511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>山西省</td>\n",
       "      <td>3299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>内蒙古自治区</td>\n",
       "      <td>4538</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       城市  平均工资\n",
       "0     北京市  9227\n",
       "1     天津市  5729\n",
       "2     河北省  4511\n",
       "3     山西省  3299\n",
       "4  内蒙古自治区  4538"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1176f4e10>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['平均工资'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>平均工资</th>\n",
       "      <th>排名</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>城市</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>北京市</th>\n",
       "      <td>9227</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>上海市</th>\n",
       "      <td>8664</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>重庆市</th>\n",
       "      <td>6181</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>天津市</th>\n",
       "      <td>5729</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>西藏自治区</th>\n",
       "      <td>5550</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       平均工资  排名\n",
       "城市             \n",
       "北京市    9227   1\n",
       "上海市    8664   2\n",
       "重庆市    6181   3\n",
       "天津市    5729   4\n",
       "西藏自治区  5550   5"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "data2 = data.sort_values(by='平均工资', ascending=False)\n",
    "#data2.index = np.arange(1, len(data2)+1)\n",
    "data2['排名'] = np.arange(1, len(data2)+1)\n",
    "data2 = data2.set_index('城市')\n",
    "data2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11a0c0ef0>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data2['平均工资'][:10].plot(kind='bar')     "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "上面画出的图不只出现了图，解决办法：导入matplotlib.pyplot，然后调用plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "data2['平均工资'][:10].plot(kind='bar')     \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "上面画出的图中文乱码，解决办法：设置字体       mac下设置比较麻烦，这里我就不进行配置了"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plt.rc('font', **{'family': \"Microsoft Yahei, SimHei\"})\n",
    "data2['平均工资'][:10].plot(kind='bar')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data2[['平均工资']].boxplot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data2[['平均工资']].hist()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.858196</td>\n",
       "      <td>0.070967</td>\n",
       "      <td>0.204869</td>\n",
       "      <td>0.027588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.869374</td>\n",
       "      <td>0.400640</td>\n",
       "      <td>0.214854</td>\n",
       "      <td>0.640225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.361789</td>\n",
       "      <td>0.017236</td>\n",
       "      <td>0.460080</td>\n",
       "      <td>0.775333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.453673</td>\n",
       "      <td>0.272602</td>\n",
       "      <td>0.996006</td>\n",
       "      <td>0.374573</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.913877</td>\n",
       "      <td>0.929141</td>\n",
       "      <td>0.600742</td>\n",
       "      <td>0.075378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.128329</td>\n",
       "      <td>0.883840</td>\n",
       "      <td>0.881779</td>\n",
       "      <td>0.746314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.665850</td>\n",
       "      <td>0.557459</td>\n",
       "      <td>0.948802</td>\n",
       "      <td>0.181428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.033188</td>\n",
       "      <td>0.566821</td>\n",
       "      <td>0.271778</td>\n",
       "      <td>0.316478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.765067</td>\n",
       "      <td>0.838131</td>\n",
       "      <td>0.463347</td>\n",
       "      <td>0.422035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.721481</td>\n",
       "      <td>0.981777</td>\n",
       "      <td>0.502587</td>\n",
       "      <td>0.336241</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          A         B         C         D\n",
       "0  0.858196  0.070967  0.204869  0.027588\n",
       "1  0.869374  0.400640  0.214854  0.640225\n",
       "2  0.361789  0.017236  0.460080  0.775333\n",
       "3  0.453673  0.272602  0.996006  0.374573\n",
       "4  0.913877  0.929141  0.600742  0.075378\n",
       "5  0.128329  0.883840  0.881779  0.746314\n",
       "6  0.665850  0.557459  0.948802  0.181428\n",
       "7  0.033188  0.566821  0.271778  0.316478\n",
       "8  0.765067  0.838131  0.463347  0.422035\n",
       "9  0.721481  0.981777  0.502587  0.336241"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "data = pd.DataFrame(np.random.rand(10, 4), columns=list('ABCD'))\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.plot(kind='bar')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.plot(kind='barh')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 叠加\n",
    "data.plot(kind='barh', stacked=True)\n",
    "plt.show()  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 保存图为图片或pdf--plt.savefig()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.plot(kind='barh', stacked=True)\n",
    "plt.savefig('test.png')        #  jpg  png   pdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.plot(kind='barh', stacked=True)\n",
    "plt.savefig('test.pdf')        #  jpg  png   pdf"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
